Articles | Volume 20, issue 14
https://doi.org/10.5194/bg-20-2941-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-2941-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Reviews and syntheses: Recent advances in microwave remote sensing in support of terrestrial carbon cycle science in Arctic–boreal regions
Alex Mavrovic
CORRESPONDING AUTHOR
Département des sciences de l'environnement, Université du Québec à Trois-Rivières,
Trois-Rivières, Quebec, Canada
Centre d'Études Nordiques, Université Laval, Québec,
Quebec, Canada
Canadian High Arctic Research Station Campus, Polar Knowledge Canada, Cambridge Bay, Nunavut, Canada
Département de géographie, Université de Montréal, Montréal, Quebec, Canada
Oliver Sonnentag
Centre d'Études Nordiques, Université Laval, Québec,
Quebec, Canada
Département de géographie, Université de Montréal, Montréal, Quebec, Canada
Juha Lemmetyinen
Finnish Meteorological Institute, Helsinki, Finland
Jennifer L. Baltzer
Biology Department, Wilfrid Laurier University, Waterloo, Ontario,
Canada
Christophe Kinnard
Département des sciences de l'environnement, Université du Québec à Trois-Rivières,
Trois-Rivières, Quebec, Canada
Centre d'Études Nordiques, Université Laval, Québec,
Quebec, Canada
Alexandre Roy
Département des sciences de l'environnement, Université du Québec à Trois-Rivières,
Trois-Rivières, Quebec, Canada
Centre d'Études Nordiques, Université Laval, Québec,
Quebec, Canada
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Cited
11 citations as recorded by crossref.
- Assessment of L-Band Passive Microwave Soil Moisture Retrievals Against In Situ Measurements in the North American Boreal Biome A. Gorrab et al. 10.1109/JSTARS.2025.3569063
- Research Progress of Plant Available Carbon in the Earth's Carbon Cycle X. Sun et al. 10.1051/e3sconf/202561701008
- Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations K. Donahue et al. 10.3389/fdata.2023.1243559
- OpenForest: a data catalog for machine learning in forest monitoring A. Ouaknine et al. 10.1017/eds.2024.53
- Assessing the SMAP Level-4 Carbon Product Over the Arctic and Subarctic Zones R. Madelon et al. 10.1109/JSTARS.2025.3555850
- Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan D. Putra & P. Hsu 10.3390/ijgi13050162
- Estimating and Downscaling ESA-CCI Soil Moisture Using Multi-Source Remote Sensing Images and Stacking-Based Ensemble Learning Algorithms in the Shandian River Basin, China L. Wang & Y. Gao 10.3390/rs17040716
- Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data A. Khan et al. 10.1007/s44274-025-00256-0
- Near Real‐Time Mapping of All‐Sky Land Surface Temperature From GOES‐R Using Machine Learning S. Ranjbar et al. 10.1029/2024JH000464
- Mapping of lakes and heave mounds in the Arctic using synthetic aperture radar and interferometric synthetic aperture radar data with deep learning technologies A. Yuriev et al. 10.21285/2686-9993-2024-47-4-417-429
- Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets H. Mizuochi et al. 10.1186/s40645-024-00614-1
10 citations as recorded by crossref.
- Assessment of L-Band Passive Microwave Soil Moisture Retrievals Against In Situ Measurements in the North American Boreal Biome A. Gorrab et al. 10.1109/JSTARS.2025.3569063
- Research Progress of Plant Available Carbon in the Earth's Carbon Cycle X. Sun et al. 10.1051/e3sconf/202561701008
- Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations K. Donahue et al. 10.3389/fdata.2023.1243559
- OpenForest: a data catalog for machine learning in forest monitoring A. Ouaknine et al. 10.1017/eds.2024.53
- Assessing the SMAP Level-4 Carbon Product Over the Arctic and Subarctic Zones R. Madelon et al. 10.1109/JSTARS.2025.3555850
- Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan D. Putra & P. Hsu 10.3390/ijgi13050162
- Estimating and Downscaling ESA-CCI Soil Moisture Using Multi-Source Remote Sensing Images and Stacking-Based Ensemble Learning Algorithms in the Shandian River Basin, China L. Wang & Y. Gao 10.3390/rs17040716
- Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data A. Khan et al. 10.1007/s44274-025-00256-0
- Near Real‐Time Mapping of All‐Sky Land Surface Temperature From GOES‐R Using Machine Learning S. Ranjbar et al. 10.1029/2024JH000464
- Mapping of lakes and heave mounds in the Arctic using synthetic aperture radar and interferometric synthetic aperture radar data with deep learning technologies A. Yuriev et al. 10.21285/2686-9993-2024-47-4-417-429
Latest update: 18 Jun 2025
Short summary
This review supports the integration of microwave spaceborne information into carbon cycle science for Arctic–boreal regions. The microwave data record spans multiple decades with frequent global observations of soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties, and land cover. This record holds substantial unexploited potential to better understand carbon cycle processes.
This review supports the integration of microwave spaceborne information into carbon cycle...
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